A Practical Approach To Verifying Structural Invariants In Colored Petri Nets
Lorenzo Capra
Abstract
Structural analysis is a core method in Petri Net (PN) research, offering a perspective complementary to state-space techniques while avoiding many of their limitations. It is well studied for classical PNs but far less for High-Level Petri Nets (HLPN). Symmetric Nets (SN), a common HLPN formalism, use compact annotations to encode behavioral symmetries, enabling the construction of a symbolic reachability graph (and a lumped Markov chain in stochastic SN) and the execution of symbolic discrete-event simulations. During the past two decades, structural techniques tailored to SN have been developed, notably supported by the SNexpression tool. This tool implements a formal calculus designed for the computation of symbolic structural relations, including, but not limited to, conflict relations and causal dependencies. Here, we focus on using this calculus to verify semi-automatically symbolic structural invariants, a task currently feasible only for certain restricted SN subclasses. We focus specifically on (semi)flows and briefly discuss an approach through which a flow generative family can be generated, at least theoretically. We further briefly outline a framework for the formal verification of a broader class of invariant properties. An extended formulation of the SN formalism is employed, which demonstrably satisfies the closure property with respect to fundamental functional operators. The core concepts are elucidated by means of representative examples throughout the exposition.
Create a lesson
Related papers
Exact rational certificates and a Lean formalization of the eleven-variable Vasc inequality
Dakai Guo, Ruichen Qiu, Yichuan Cao et al.
RISR: Residual-Informed Scientific Equation Discovery with Large Language Models
Haobo Li, Wenshuo Zhang, Wenxiao Zhao et al.
A Finite Certificate for the Positive n=11 Vasc Inequality
Tiancheng Xu, Wensheng Yu
Parallel Integration over Simple Radical Extensions III: Antiderivatives in Terms of Special Functions
Sam Blake
Root isolation for analytic functions using cubic hermite interpolation
Christophe Raffalli
CPUNeSy: Controlling Model Writes for Reliable Neuro-Symbolic Reasoning
Zeyan Li, Siyuan Qiu, Shuai Zhao et al.